MetNet-2 and NWP models gather empirical observations in order to obtain an initial state of the atmosphere as a basis for their forecasts. Observations come from a variety of sensors that are located on the ground in weather stations, on satellites, on airplanes and balloons, and on ocean buoys, among others. An important source of observations in our framework are those coming from ground radars that densely populate the Continental United States. The reflectivity, measured by these radars, estimates the amount of precipitation at a given time and location. The estimates are made every few minutes and have a relatively high spatial resolution of 1 km \(\times \)  1 km. In our framework, we use two types of precipitation measures: instantaneous precipitation that comes from the radar reflectivity at a temporal frequency of two minutes; and hourly cumulative precipitation that represents the amount of precipitation over the preceding hour. In the latter, rain gauges at weather stations are used to further corroborate the radar measurements improving the data reliability. Both measures are provided by the Multi-Radar Multi-System (MRMS) [1]}.
While radar measurements provide information about the measures of precipitation, they do not describe the many other variables of the atmosphere, such as pressure, temperature and wind velocity and direction. Since the latter are not readily available, in order to incorporate them in our framework, we use the available set of atmospheric observations that result from the data assimilation process in the NWP model HRRR. This process uses various statistical and physics-based techniques to incorporate observations from the atmospheric sensors including those coming from the radars themselves. The resulting state is the starting point for NWP's simulation and we also adopt that state as an input for MetNet-2 to provide the model with more thorough information about the initial state of the atmosphere.
In addition to radar and assimilation features, MetNet-2 also receives space-time coordinates for longitude, latitude, elevation and forecast time [2]} as well as optical satellite imagery; see Supplement  for a full description of data inputs.
